Probing the functional impact of sequence variation on p53-DNA interactions using a novel microsphere assay for protein-DNA binding with human cell extracts. by Maher A Noureddine, Daniel Menendez, Michelle R Campbell, Omari J Bandele, Monica M Horvath, Xuting Wang, Gary S Pittman, Brian N Chorley, Michael A Resnick, Douglas A Bell

“…Using a set of 26 bona fide REs, we observed distinct binding patterns characteristic of transiently expressed wild type and mutant p53s. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

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Cancelable finger vein authentication using multidimensional scaling based on deep learning by Mohamed Hammad, Mohammed ElAffendi, Ahmed A. Abd El-Latif

“…We evaluated our system on three publicly available datasets for finger veins using various performance metrics, including accuracy, precision, recall, and equal error rate (EER). …”
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Paraphrase detection for Urdu language text using fine-tune BiLSTM framework by Muhammad Ali Aslam, Khairullah Khan, Wahab Khan, Sajid Ullah Khan, Abdullah Albanyan, Shabbab Ali Algamdi

“…Our approach employs word embeddings and text preprocessing techniques like tokenization, stop-word removal, and label encoding to effectively handle Urdu’s morphological variations. The BiLSTM network sequentially processes the input, leveraging both forward and backward contextual information to encode the complex syntactic and semantic patterns inherent in Urdu text. …”
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Relating satellite NO2 tropospheric columns to near-surface concentrations: implications from ground-based MAX-DOAS NO2 vertical profile observations by Bowen Chang, Haoran Liu, Chengxin Zhang, Chengzhi Xing, Wei Tan, Cheng Liu

“…In this study, the correlation between CNO2 and SNO2 is examined using vertical profile observations from China’s MAX-DOAS network. Cloud cover and air convection substantially weaken (R = −0.68) and strengthen (R = 0.71) the CNO2-SNO2 correlation, respectively. …”
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Tracing Student Learning Outcome at Historically Black Colleges and Universities via Deep Knowledge Tracing by Ming-Mu Kuo, Xiangfang Li, Pamela H. Obiomon, Lijun Qian, Xishuang Dong

“…Specifically, Dynamic Key-Value Memory Network (DKVMN) outperforms other models due to its advanced mechanisms for capturing student learning patterns. …”
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RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline by Guo Gan, Guang Yang, Zhengrong Liu, Ruiyan Xia, Zhenqing Zhu, Yuke Qiu, Hong Zhou, Yangwei Ying

“…Human pose comparison involves measuring the similarities in body postures between individuals to understand movement patterns and interactions, yet existing methods are often insufficiently robust and flexible. …”
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Surgeons are apprehensive to use DCD lungs despite similar post-transplant outcomes: A 20-year UNOS retrospective analysis by J. Sam Meyer, MSc, Oliver K. Jawitz, MD, MHS, Yury Peysakhovich, MD, Dan Aravot, MD, Matthew G. Hartwig, MD, MHS, Yaron D. Barac, MD, PhD

“…Methods: We performed a retrospective analysis of United Network for Organs Sharing (UNOS) Organ Procurement and Transplantation Network/UNOS STAR (Standard Analysis and Research) database. …”
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Development of a diagnostic classification model for lateral cephalograms based on multitask learning by Qiao Chang, Shaofeng Wang, Fan Wang, Beiwen Gong, Yajie Wang, Feifei Zuo, Xianju Xie, Yuxing Bai

“…The multitask learning classification model was constructed based on the ResNeXt50_32 × 4d network and consisted of shared layers and task-specific layers. …”
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Enhancing LoRa-Based Outdoor Localization Accuracy Using Machine Learning by Nur Kelesoglu, Marzena Halama, Anna Strzoda

“…This architecture integrates the strengths of Deep Learning and tree-based models, aiming to capture both temporal signal patterns and structured input correlations for improved localization accuracy. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…At the core of our framework is a hybrid neural network architecture, which leverages Long Short-Term Memory (LSTM) units to handle sequential dependencies alongside dense layers that model complex interactions among features. …”
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Enhancing Time Series Product Demand Forecasting With Hybrid Attention-Based Deep Learning Models by Xuguang Zhang, Pan Li, Xu Han, Yongbin Yang, Yiwen Cui

“…This paper presents a novel approach to time series forecasting by leveraging advanced deep learning techniques, specifically focusing on hybrid models that combine attention mechanisms with traditional recurrent neural networks. Our proposed method, the Hybrid Attention-based Long Short-Term Memory (HA-LSTM) network, integrates multi-head self-attention modules with LSTM layers to capture both long-term dependencies and local temporal patterns in time series data. …”
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Optimizing HVAC energy efficiency in low-energy buildings: a comparative analysis of reinforcement learning control strategies under Tehran climate conditions by Mohammad Anvar Adibhesami, Amir Hassanzadeh

“…We conducted comprehensive simulation assessments using the EnergyPlus and HoneybeeGym platforms to evaluate two distinct reinforcement learning models: traditional Q-learning (Model A) and deep reinforcement learning (DRL) with neural networks (Model B). …”
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Chinese Paper-Cutting Style Transfer via Vision Transformer by Chao Wu, Yao Ren, Yuying Zhou, Ming Lou, Qing Zhang

“…To further embody the symmetrical structures and hollowed hierarchical patterns intrinsic to Chinese paper-cutting, the multi-level feature contrastive learning module is designed based on a contrastive learning strategy. …”
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Integration of whole genome resequencing and transcriptome sequencing to identify candidate genes for tall and short traits in Baicheng Fatty chickens by Jiaqi Li, Kaixu Chen, Mengting Zhu, Jingdong Bi, Honggang Tang, Weiyi Gao

“…These genes may influence the growth and developmental patterns of skeletal structures, though their regulatory mechanisms require further investigation. …”
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Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset by Ahmed E. Mansour Atwa, El-Sayed Atlam, Ali Ahmed, Mohamed Ahmed Atwa, Elsaid Md. Abdelrahim, Ali I. Siam

“…Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. <b>Methods</b>: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. …”
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Influence of boundary conditions and blood rheology on indices of wall shear stress from IVUS-based patient-specific stented coronary artery simulations by R. Patrick McCarthy, Peter J. Mason, David S. Marks, John F. LaDisa

“…Coronary stenting results in altered arterial geometry, local blood flow patterns, and wall shear stress (WSS), all of which can influence restenosis. …”
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Seq2Seq-based GRU autoencoder for anomaly detection and failure identification in coal mining hydraulic support systems by Kai Zhan, Cong Wang, Xigui Zheng, Chao Kong, Guangming Li, Wei Xin, Longhe Liu

“…Our proposed Recurrent Reconstruction Network model demonstrated excellent performance in complex coal mine hydraulic support data, effectively identifying anomalous regions and potential equipment failure characteristics while revealing potential deviations between model predictions and actual data, demonstrating its superior learning capability for periodic data patterns and equipment failure characteristics. …”
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Classification of tomato leaf disease using Transductive Long Short-Term Memory with an attention mechanism by Aarthi Chelladurai, D.P. Manoj Kumar, S. S. Askar, Mohamed Abouhawwash, Mohamed Abouhawwash

“…This can involve leveraging the relationships and patterns observed within the dataset. The T-LSTM is based on the transductive learning approach and the scaled dot product attention evaluates the weights of each step based on the hidden state and image patches which helps in effective classification. …”
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Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates by Ocheme Anthony Ekle, William Eberle, Jared Christopher

“…Real-time anomaly detection in large, dynamic graph networks is crucial for real-world applications such as network intrusion prevention, fraud transaction identification, fake news detection in social networks, and uncovering abnormal communication patterns. …”
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